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GMD | Articles | Volume 12, issue 2
Geosci. Model Dev., 12, 735–747, 2019
https://doi.org/10.5194/gmd-12-735-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 735–747, 2019
https://doi.org/10.5194/gmd-12-735-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 19 Feb 2019

Methods for assessment of models | 19 Feb 2019

Similarities within a multi-model ensemble: functional data analysis framework

Eva Holtanová et al.
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Cited articles  
Annan, J. D. and Hargreaves, J. C.: On the meaning of independence in climate science, Earth Syst. Dynam., 8, 211–224, https://doi.org/10.5194/esd-8-211-2017, 2017. 
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Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model projections of changes in European climate during this century, Clim. Change, 81(Supp. 1), 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007. 
Craven, P. and Wahba, G.: Smoothing noisy data with spline functions, Numerische Mathematik, 31, 377–403, 1978. 
Crhová, L. and Holtanová, E.: Simulated relationship between air temperature and precipitation over Europe: sensitivity to the choice of RCM and GCM, Int. J. Clim., 38, 1595–1604, https://doi.org/10.1002/joc.5256, 2018. 
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We present a methodological framework for the analysis of climate model uncertainty based on the functional data analysis approach, an emerging statistical field. The novel method investigates the multi-model spread, taking into account the behavior of entire simulated climatic time series, encompassing both past and future periods. We also introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm that enables an unambiguous interpretation.
We present a methodological framework for the analysis of climate model uncertainty based on the...
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